DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion
Abstract High-resolution images encapsulate abundant geographical information; however, precise semantic segmentation is essential for effective remote sensing image interpretation. Remote sensing semantic segmentation categorizes pixel-level image information into distinct land cover types, providi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-13236-4 |
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| author | Yong Ji Wenbin Shi Jingsheng Lei Jiayin Ding |
| author_facet | Yong Ji Wenbin Shi Jingsheng Lei Jiayin Ding |
| author_sort | Yong Ji |
| collection | DOAJ |
| description | Abstract High-resolution images encapsulate abundant geographical information; however, precise semantic segmentation is essential for effective remote sensing image interpretation. Remote sensing semantic segmentation categorizes pixel-level image information into distinct land cover types, providing essential support for urban planning, resource management, and environmental monitoring. However, existing approaches encounter two major challenges: insufficient retention of fine-grained local details and suboptimal global contextual modeling, especially in intricate and high-resolution remote sensing scenarios. These limitations result in fragmented object boundaries, degradation of small-scale structures, and challenges in comprehending large-scale spatial dependencies. To address these limitations, we introduce DBRSNet, an advanced dual-branch remote sensing segmentation framework that integrates feature interaction with multi-scale feature fusion. In DBRSNet, the Feature-Guided Selection Module (FGSM) adaptively integrates complementary features from CNN and Transformer branches, while the Convolutional Attention Integration Module (CAIM) enhances global dependencies and spectral correlations, ensuring a more comprehensive feature representation. Extensive evaluations on the ISPRS Vaihingen and ISPRS Potsdam datasets validate that DBRSNet surpasses 14 cutting-edge remote sensing segmentation models across all assessment metrics, highlighting its exceptional performance and competitiveness. |
| format | Article |
| id | doaj-art-8ca3c8178bb940a9b5447f340df12f7b |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8ca3c8178bb940a9b5447f340df12f7b2025-08-20T03:05:27ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-13236-4DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusionYong Ji0Wenbin Shi1Jingsheng Lei2Jiayin Ding3School of Computer Science and Technology, Zhejiang University of Science and TechnologySchool of Computer, Hangzhou Dianzi UniversitySchool of Computer Science and Technology, Zhejiang University of Science and TechnologySchool of Computer Science and Technology, Zhejiang University of Science and TechnologyAbstract High-resolution images encapsulate abundant geographical information; however, precise semantic segmentation is essential for effective remote sensing image interpretation. Remote sensing semantic segmentation categorizes pixel-level image information into distinct land cover types, providing essential support for urban planning, resource management, and environmental monitoring. However, existing approaches encounter two major challenges: insufficient retention of fine-grained local details and suboptimal global contextual modeling, especially in intricate and high-resolution remote sensing scenarios. These limitations result in fragmented object boundaries, degradation of small-scale structures, and challenges in comprehending large-scale spatial dependencies. To address these limitations, we introduce DBRSNet, an advanced dual-branch remote sensing segmentation framework that integrates feature interaction with multi-scale feature fusion. In DBRSNet, the Feature-Guided Selection Module (FGSM) adaptively integrates complementary features from CNN and Transformer branches, while the Convolutional Attention Integration Module (CAIM) enhances global dependencies and spectral correlations, ensuring a more comprehensive feature representation. Extensive evaluations on the ISPRS Vaihingen and ISPRS Potsdam datasets validate that DBRSNet surpasses 14 cutting-edge remote sensing segmentation models across all assessment metrics, highlighting its exceptional performance and competitiveness.https://doi.org/10.1038/s41598-025-13236-4 |
| spellingShingle | Yong Ji Wenbin Shi Jingsheng Lei Jiayin Ding DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion Scientific Reports |
| title | DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion |
| title_full | DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion |
| title_fullStr | DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion |
| title_full_unstemmed | DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion |
| title_short | DBRSNet: a dual-branch remote sensing image segmentation model based on feature interaction and multi-scale feature fusion |
| title_sort | dbrsnet a dual branch remote sensing image segmentation model based on feature interaction and multi scale feature fusion |
| url | https://doi.org/10.1038/s41598-025-13236-4 |
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